Optimizes audio codec selection with statistical guarantees.
problem Selecting the best audio encoding scheme for various data types.
method Supervised learning with uniform convergence theory.
result Rigorous statistical guarantees for codec selection.
Improved hybrid image compression using deep learning and traditional codecs.
problem Efficiently compressing images with high quality and low bitrates.
method Combining CNN for compact representation, FLIF for base layer, and BPG for enhancement layer.
result Outperforms state-of-the-art schemes in PSNR and MS-SSIM metrics.
Deep learning improves video coding efficiency without client-side changes.
problem Improving video coding efficiency without altering existing standards.
method Cascaded downscaling neural networks coupled with adaptive precoding mode selection.
result 15% to 45% rate reduction in FHD and UHD content encoding.
Neural network VQ-VAE with WaveNet decodes speech at 1.6 kbps with high quality.
problem Efficiently transmitting and storing speech signals at low bit-rates.
method VQ-VAE and WaveNet architecture for speech coding.
result Speech coding at 1.6 kbps with perceptual quality between MELP and AMR-WB.
C3 compresses images and videos with low complexity and high performance.
problem High complexity and low performance in neural compression models.
method Overfits a small model to each image or video separately, improving RD performance with low complexity.
result Matches the RD performance of state-of-the-art neural and video codecs with significantly lower decoding complexity.
A novel method compresses point cloud attributes by folding them onto a 2D grid.
problem Efficiently compressing point cloud attributes for storage and transmission.
method Interpreting point clouds as 2D manifolds, folding onto a grid, and mapping attributes to the grid using optimized methods.
result The proposed folding-based approach achieves performance comparable to state-of-the-art codecs.
We present a machine learning-based approach to lossy image compression which outperforms all existing codecs, while running in real-time. Our algorithm typically produces files 2.5 times smaller than JPEG and JPEG 2000, 2 times smaller than WebP, and 1.7 times smaller than BPG on datasets of generic images across all …
New video compression algorithm outperforms existing codecs.
problem Efficient video compression with low latency.
method End-to-end learned video coding architecture.
result Significantly outperforms existing codecs across bitrate ranges.
Paper compresses neural network weight-updates for image artifacts removal.
problem Efficiently compressing neural network weight-updates for image artifacts removal.
method Fine-tuning a pre-trained artifact removal network on target data with a compression objective that encourages sparse and quantized weight-updates.
result Achieves reconstruction quality comparable to traditional codecs at comparable bitrates.
Neural codec for high-fidelity audio compression.
problem Efficiently compress audio while maintaining high quality.
method End-to-end neural network architecture with quantized latent space, single multiscale spectrogram adversary, loss balancer mechanism, and lightweight Transformer compression.
result 40% compression with no loss in quality, faster than real-time.
Modality-agnostic compression improves across diverse data types.
problem Efficiently compressing data across multiple modalities.
method Functional view of data, Implicit Neural Representation (INR), modality-agnostic latent representations, variational compression.
result Improved performance compared to existing methods, especially for diverse modalities.
We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements and content types create a need for compression algorithms which are more flexible than existing codecs. Autoencoders have the potential to ad…
End-to-end deep generative model for video compression.
problem Efficiently compressing video data with deep learning.
method Variational autoencoder (VAE) for sequential data, combined with neural image compression techniques.
result Our model achieves competitive rate-distortion results on diverse video content.
This work compresses sequences by treating them as continuous-time processes, enabling efficient discretization.
problem Efficient compression of sequences, especially with deep learning models that scale with sequence length.
method Treat sequences as continuous-time processes, learn efficient discretization, and decode at different time intervals.
result Automatic bit rate reductions in video and motion capture sequences using learned discretization.
New method compresses facial videos using GANs and latent space optimization.
problem Efficiently compressing facial videos at low bit rates.
method Leverages StyleGAN for latent space representation and compression, learns optimal compression through entropy model and perceptual loss.
result Significantly reduces perceptual distortion at low bit rates compared to state-of-the-art codecs.
Develops algorithms for efficient visual data compression and search.
problem High-dimensional, large-scale visual data representation.
method Discrete synthesis and analysis models, RRQ and ML-STC frameworks.
result Fast query times and shorter database storage for similarity search.
FEDS distills LIC model knowledge into a lightweight student for efficient compression.
problem Efficiently compress images with high performance and low resources.
method FEDS combines feature alignment and entropy-based loss for lightweight compression.
result Student model matches teacher's performance while reducing parameters and speeding up.
Researchers forecast VoIP traffic in mobile networks using multivariate time series analysis.
problem Predicting VoIP traffic behavior in real mobile networks for better resource allocation.
method Multivariate time series analysis, Vector Autoregressive models, machine learning techniques.
result Forecasting accuracy and insights into VoIP traffic dynamics.
Paper presents a new video compression method using autoencoders.
problem Efficient video compression with reduced quality loss.
method 3D autoencoder with discrete latent space and autoregressive prior trained jointly.
result Method outperforms state-of-the-art learned video compression networks.
The paper proposes a new framework to generate synthetic data with human-like imperfections to prevent model collapse.
problem Model collapse due to statistical optimization of synthetic data.
method Introduces Prompt-driven Cognitive Computing Framework (PMCSF) with Cognitive State Decoder (CSD) and Cognitive Text Encoder (CTE).
result The framework generates text with cognitive imperfections, reducing maximum drawdown and delivering defensive alpha.
FibQuant improves KV-cache compression for long-context inference.
problem Memory traffic bottleneck in long-context inference due to KV cache growth.
method Introduces FibQuant, a universal vector quantizer that combines Beta-quantile radii, Fibonacci/Roberts-Kronecker directions, and Lloyd-Max refinement.
result FibQuant achieves high compression rates with minimal loss in attention cosine similarity.
New method estimates selection event for black-box models.
problem Infeasibility of conditional selective inference due to unavailable selection event.
method Bootstrapping to estimate selection event and conditional distribution.
result Feasibility of conditional selective inference for models without exact selection event.
Cluster stability selection improves feature selection in correlated data.
problem Feature selection stability in correlated data.
method Cluster stability selection exploiting known cluster structure.
result Better predictive performance than lasso alone and stability selection.
New method corrects selection bias in post-selective inference for Group LASSO.
problem Inference after Group LASSO selection is unreliable.
method Develops a consistent, post-selective Bayesian method to adjust for selection bias.
result Corrects bias in recovering effects of selected variables.
Proposes PEMI for online selective conformal prediction with asymmetric rules.
problem Challenges of handling asymmetric selection mechanisms in online selective conformal prediction.
method PEMI: permutation-based framework for selective conformal prediction with arbitrary asymmetric selection rules.
result Achieves exact selection-conditional coverage for any asymmetric selection mechanism and any prediction model.
The paper addresses errors in online selective conformal prediction and proposes new strategies to ensure valid inference.
problem Online selective conformal prediction's exchangeability issues and false coverage rate control problems.
method Evaluation and correction of existing calibration selection strategies, proposing new ones that preserve exchangeability.
result Novel calibration selection strategies ensure both selection-conditional coverage and FCR control.
This work develops scalable model selection methods with fast update and selection.
problem Efficient model selection for large pools of candidate models.
method Isolated model embedding, which supports asymptotically fast update and selection.
result Standardized Embedder achieves competitive model selection performances.
Online method selects candidates from data streams, ensuring irreversible decisions.
problem Conformal selection's incompatibility with irreversible decisions in online scenarios.
method Online Conformal Selection with Accept-to-Reject Changes (OCS-ARC) incorporating online Benjamini-Hochberg procedure.
result OCS-ARC controls FDR at or below nominal level, improving selection power.
Paper improves k-NN predictive performance with efficient variable selection.
problem Improving predictive performance of k-NN models. method Efficient forward selection of predictor variables.
result Novel approach approaches outperformance of stepwise selection models.
Optimizes biomarker selection for cost-effective treatment rules.
problem Incorporating multiple biomarkers in treatment selection rules can be costly and reduce model performance.
method Developed procedures for estimating linear and nonlinear combinations of biomarkers using 0-norm penalized weighted classification.
result Demonstrated the importance of feature selection and marker cost in treatment selection rules.
Value selection reduces model size while maintaining accuracy.
problem Space efficiency in model size reduction.
method Two probabilistic methods based on information theory's metric: PVS and P + VS.
result Value selection achieves balance between accuracy and model size reduction.
Proposes HSIC-Lasso for selective inference in non-linear data.
problem Detecting influential features in non-linear and high-dimensional data.
method Model-free HSIC-Lasso based on truncated Gaussians and polyhedral lemma.
result Tight control of type-I error even for small sample sizes.
Paper proposes a statistical test for feature selection pipelines using selective inference.
problem Assessing the significance of feature selection pipelines in data analysis.
method Selective inference technique applied to feature selection pipelines composed of various algorithms.
result The proposed statistical test controls false positive feature selection probabilities.
New Bitcoin coin selection method improves cost savings.
problem Improving cost savings in Bitcoin coin selection.
method Coin selection with leverage, allowing user-tunable parameters.
result Natural replacement for standard knapsack method.
More powerful feature selection tests using selective inference.
problem Selection bias in feature selection leading to specious analysis.
method Conditioning on minimal selection event using Maximum Mean Discrepancy and Hilbert Schmidt Independence Criterion with multiscale bootstrap.
result Proposed test is more powerful in most scenarios.
New method improves feature selection by integrating stability paths.
problem Improving feature selection with tighter false positive control.
method Integrating stability paths to strengthen theoretical bounds on E(FP).
result Significantly more true positives with same E(FP) control.
UniFeat is an open-source Java tool for feature selection.
problem Efficient feature selection in various research areas.
method Provides a set of advanced feature selection methods.
result Facilitates rapid development of new feature selection algorithms.
We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes the distribution of a post-selection estimator conditioned on the selection event. We specialize the approach to model selection by the lasso to form valid confidence intervals for the sel…
Proposes a new method using Copula Entropy for variable selection.
problem Variable selection in machine learning and statistics.
method Copula Entropy (CE) based ranks for variable selection, model-free and tuning-free.
result CE based method selects variables more effectively and derives better interpretable results.
Efficiently selects data points for deep learning using smaller proxy models.
problem Expensive data selection methods in deep learning.
method Uses a small proxy model to perform data selection.
result Improves data selection runtime by an order of magnitude without significant accuracy loss.
Flexible selective inference using flow-based transport maps.
problem Selective inference with complex selection events.
method Flow-based generative modeling for conditional distribution approximation.
result Valid p-values and confidence sets for adaptively selected hypotheses and parameters.
Recently, many regularized procedures have been proposed for variable selection in linear regression, but their performance depends on the tuning parameter selection. Here a criterion for the tuning parameter selection is proposed, which combines the strength of both stability selection and cross-validation and therefo…
Develops a more powerful selective inference method for stepwise feature selection.
problem Loss of power in existing conditional SI methods due to over-conditioning.
method Uses homotopy continuation approach to overcome over-conditioning.
result Shows improved power and efficiency in selective inference for feature selection.
Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of feature stream. For instance, in image analysis, features are generated in groups which represent color, texture…
Paper tackles selective regression using uncertainty estimation.
problem Selective regression for machine learning models to avoid predictions when uncertain.
method Model-agnostic non-parametric uncertainty estimation.
result Superior performance compared to state-of-the-art selective regressors.
A new method selects clean samples to train DNNs with noisy labels.
problem Training deep neural networks with noisy labeled data.
method Adaptive k-set selection to choose clean samples at each epoch.
result The method guarantees performance with a theoretical bound on regret.
IWeS selects examples by entropy-based importance sampling for subset selection.
problem Efficiently selecting examples for model training in batch settings.
method IWeS uses importance sampling based on model entropy to select examples.
result IWeS outperforms other subset selection algorithms on seven datasets.
ACS is an interactive framework for model-free selection with guaranteed error control.
problem Model-free selection with rigorous error control.
method Adaptive conformal selection with human-in-the-loop data exploration and new information incorporation.
result ACS provides concrete selection algorithms for various goals, including model update/selection, diversified selection, and incorporating new data.